1. Field of the Invention
The invention relates to a source separation system for processing input signals formed by mixtures of primary signals originating from various sources and for estimating the primary signals, the system comprising a first source separation sub-assembly which, with the aid of separation coefficients, produces estimates of the primary signals, while a second sub-assembly adaptively determining the separation coefficients.
2. Description of the Related Art
There are systems which receive on their inputs signals which present themselves in the form of mixed signals formed by a superpositioning of contributions originating from various signal sources. This is shown, for example, with an antenna that receives signals originating from various transmitters, or when a microphone produces a desired speech signal mixed with undesired disturbing signals. Generally, one wishes to perfectly extract all the source signals which occur in the mixture, either completely or by an optimization of a signal-to-noise ratio.
When using various sensors which produce various mixed signals, one has sought to obtain reliable estimates of the source signals. Known techniques work with unknown mixed signals and unknown source signals, so that the separation techniques are called blind source separation techniques.
Among the known source separation structures, one may cite, for example, the prior art document "Multi-layer neural networks with a local adaptive learning rule for blind separation of source signals", A. CICHOCKI, W. KASPRZAK, S. AMARI, International Symposium on nonlinear theory and its applications (NOLTA'95) LAS VEGAS, Dec. 10-14, 1995, 1Ce-5, pages 61 to 65.
This document relates to mixtures which are hard to process, for example, because the mixed signals are very much alike or when the mixed signals have highly different levels.
Nonetheless, the structures described in this document are not suitable when the source signals are non-stationary signals. This forms a considerable drawback, because these types of signals are very often found in the concrete applications such as speech signal processing or, more generally, audio signal processing.